Hi @<1730033904972206080:profile|FantasticSeaurchin8> , can you add a code snippet that reproduces this + a log of the run?

docker compose on server:

  apiserver:
    command:
    - apiserver
    container_name: clearml-apiserver
    image: allegroai/clearml:latest
    privileged: true
    restart: unless-stopped
    volumes:
    - ${LOGS_DIR}:/var/log/clearml
    - /opt/clearml/config:/opt/clearml/config
    - ${FILESERVER_DATA_DIR}:/mnt/fileserver
    depends_on:
      - redis
      - mongo
      - elasticsearch
      - fileserver
    environment:
      CLEARML_ELASTIC_SERVICE_HOST: elasticsearch
      CLEARML_ELASTIC_SERVICE_PORT: 9200
      CLEARML_MONGODB_SERVICE_HOST: mongo
      CLEARML_MONGODB_SERVICE_PORT: 27017
      CLEARML_REDIS_SERVICE_HOST: redis
      CLEARML_REDIS_SERVICE_PORT: 6379
      CLEARML_SERVER_DEPLOYMENT_TYPE: linux
      CLEARML__apiserver__pre_populate__enabled: "true"
      CLEARML__apiserver__pre_populate__zip_files: "/opt/clearml/db-pre-populate"
      CLEARML__apiserver__pre_populate__artifacts_path: "/mnt/fileserver"
      CLEARML__services__async_urls_delete__enabled: "true"
      CLEARML__services__async_urls_delete__fileserver__url_prefixes: "[${CLEARML_FILES_HOST:-}]"
      CLEARML__secure__credentials__services_agent__user_key: ${CLEARML_AGENT_ACCESS_KEY:-}
      CLEARML__secure__credentials__services_agent__user_secret: ${CLEARML_AGENT_SECRET_KEY:-}
    ports:
    - "8008:8008"
    networks:
      clearml-backend:
          aliases:
            - servername
            - servername.company.com
      clearml-frontend:

  elasticsearch:
    networks:
      - clearml-backend
    container_name: clearml-elastic
    privileged: true
    environment:
      bootstrap.memory_lock: "true"
      cluster.name: clearml
      cluster.routing.allocation.node_initial_primaries_recoveries: "500"
      cluster.routing.allocation.disk.watermark.low: 500mb
      cluster.routing.allocation.disk.watermark.high: 500mb
      cluster.routing.allocation.disk.watermark.flood_stage: 500mb
      discovery.type: "single-node"
      http.compression_level: "7"
      node.name: clearml
      reindex.remote.whitelist: "'*.*'"
      xpack.security.enabled: "false"
    ulimits:
      memlock:
        soft: -1
        hard: -1
      nofile:
        soft: 65536
        hard: 65536
    image: docker.elastic.co/elasticsearch/elasticsearch:7.17.18
    restart: unless-stopped
    volumes:
      - ${ELASTICSEARCH_DATA_DIR}:/usr/share/elasticsearch/data
      - /usr/share/elasticsearch/logs
    # ports:
    # - "8715:9200"

  fileserver:
    networks:
      clearml-backend:
          aliases:
            - magrathea
            - magrathea.ghgsat.com
      clearml-frontend:
    command:
    - fileserver
    container_name: clearml-fileserver
    image: allegroai/clearml:latest
    privileged: true
    environment:
      # CLEARML__secure__credentials__fileserver__user_key: ${CLEARML_AGENT_ACCESS_KEY:-}
      # CLEARML__secure__credentials__fileserver__user_secret: ${CLEARML_AGENT_SECRET_KEY:-}
      CLEARML__fileserver__delete__allow_batch: "true"
      CLEARML__fileserver__auth__enabled: "false"
    restart: unless-stopped
    volumes:
    - ${LOGS_DIR}:/var/log/clearml
    - ${FILESERVER_DATA_DIR}:/mnt/fileserver
    - ${CONFIG_DIR}:/opt/clearml/config
    ports:
    - "8081:8081"

  mongo:
    networks:
      - clearml-backend
    container_name: clearml-mongo
    image: mongo:4.4.29
    privileged: true
    restart: unless-stopped
    command: --setParameter internalQueryMaxBlockingSortMemoryUsageBytes=196100200
    volumes:
    - ${MONGO_DATA_DIR}/db:/data/db
    - ${MONGO_DATA_DIR}/configdb:/data/configdb
    ports:
    - "8714:27017"

  redis:
    networks:
      - clearml-backend
    container_name: clearml-redis
    privileged: true
    image: redis:6.2
    restart: unless-stopped
    volumes:
    - ${REDIS_DATA_DIR}:/data
    ports:
    - "8713:6379"

  webserver:
    command:
    - webserver
    container_name: clearml-webserver
    privileged: true
    # environment:
    #  CLEARML_SERVER_SUB_PATH : clearml-web # Allow Clearml to be served with a URL path prefix.
    image: allegroai/clearml:latest
    restart: unless-stopped
    depends_on:
      - apiserver
    ports:
    - "8612:80"
    networks:
      - clearml-backend
      - clearml-frontend

  async_delete:
    depends_on:
      - apiserver
      - redis
      - mongo
      - elasticsearch
      - fileserver
    container_name: async_delete
    image: allegroai/clearml:latest
    privileged: true
    networks:
      - clearml-backend
    restart: unless-stopped
    environment:
      CLEARML_ELASTIC_SERVICE_HOST: elasticsearch
      CLEARML_ELASTIC_SERVICE_PORT: 9200
      CLEARML_MONGODB_SERVICE_HOST: mongo
      CLEARML_MONGODB_SERVICE_PORT: 27017
      CLEARML_REDIS_SERVICE_HOST: redis
      CLEARML_REDIS_SERVICE_PORT: 6379
      PYTHONPATH: /opt/clearml/apiserver
      CLEARML__services__async_urls_delete__fileserver__url_prefixes: "[${CLEARML_FILES_HOST:-}]"
    entrypoint:
      - python3
      - -m
      - jobs.async_urls_delete
      - --fileserver-host
      - 

    volumes:
      - ${LOGS_DIR}:/var/log/clearml
      - /opt/clearml/config:/opt/clearml/config

  agent-services:
    networks:
      clearml-backend:
        aliases:
            - servername
            - servername.company.com
    container_name: clearml-agent-services
    image: allegroai/clearml-agent-services:latest
    deploy:
      restart_policy:
        condition: on-failure
    privileged: true
    environment:
      CLEARML__AGENT__FORCE_GIT_SSH_PROTOCOL: true
      CLEARML__AGENT__ENABLE_GIT_ASK_PASS: true
      CLEARML__AGENT__GIT_HOST: repo
      CLEARML_WEB_HOST: 

      CLEARML_API_HOST: 

      CLEARML_FILES_HOST: 

      CLEARML_API_ACCESS_KEY: ${CLEARML_AGENT_ACCESS_KEY}
      CLEARML_API_SECRET_KEY: ${CLEARML_AGENT_SECRET_KEY}
      CLEARML_AGENT_GIT_USER: ${CLEARML_AGENT_GIT_USER}
      CLEARML_AGENT_GIT_PASS: ${CLEARML_AGENT_GIT_PASS}
      CLEARML_AGENT_UPDATE_VERSION: ${CLEARML_AGENT_UPDATE_VERSION:->=0.17.0}
      CLEARML_AGENT_DEFAULT_BASE_DOCKER: "ubuntu:18.04"
      AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID:-}
      AWS_SECRET_ACCESS_KEY: ${AWS_SECRET_ACCESS_KEY:-}
      AWS_DEFAULT_REGION: ${AWS_DEFAULT_REGION:-}
      # AZURE_STORAGE_ACCOUNT: ${AZURE_STORAGE_ACCOUNT:-}
      # AZURE_STORAGE_KEY: ${AZURE_STORAGE_KEY:-}
      GOOGLE_APPLICATION_CREDENTIALS: ${GOOGLE_APPLICATION_CREDENTIALS:-}
      CLEARML_WORKER_ID: "clearml-services"
      CLEARML_AGENT_DOCKER_HOST_MOUNT: "${AGENT_DIR}:/root/.clearml"
      SHUTDOWN_IF_NO_ACCESS_KEY: 1
    volumes:
      - /var/run/docker.sock:/var/run/docker.sock
      - ${AGENT_DIR}:/root/.clearml
      - /var/cache/analytics_shared:/var/cache/analytics_shared
    depends_on:
      - apiserver
    entrypoint: >
      bash -c "curl --retry 10 --retry-delay 10 --retry-connrefused '

' && /usr/agent/entrypoint.sh"

networks:
  clearml-backend:
    driver: bridge
  clearml-frontend:
    driver: bridge

sure!

So this is how im queuing the job:

task = Task.create(
    project_name="multiclass-classifier",
    task_name="training",
    repo="reponame_url",
    branch="branch_name",
    script='training_script_name',
    packages=package_list,
    docker="python:3.11",
    docker_args="--privileged"

)
task.enqueue(task, "services")  # services queue is the one with a remote worker

local conf

api {
    # Notice: 'host' is the api server (default port 8008), not the web server.
    api_server: 

    web_server: 

    files_server: 

    # Credentials are generated using the webapp, 

    # Override with os environment: CLEARML_API_ACCESS_KEY / CLEARML_API_SECRET_KEY
    credentials {"access_key": "somekey", "secret_key": "somekey"}
}

 # Default Task output_uri. if output_uri is not provided to Task.init, default_output_uri will be used instead.
        default_output_uri:'

'

this is how im initializing before calling my training function. this is inside my training_script:

task = Task.init(project_name=project_name, task_name=task_name, output_uri=True) 

this task is currently running, i obfuscated some personal info

which logs are helpful? console output, fileserver, or api?

my main issue is that i can see that the model artefacts are here file:///root/.clearml/venvs-builds/3.11/task_repository/my_awesome_facility_model/checkpoint-33/scheduler.pt
which i believe is not persistent/retrievable with an artefact id

can you attach the log of the task where ClearML SDK fails to resolve the output_uri?

the console output? or?

it is reachable to get data for training as image data is saved on the file server

way im attempting to access with an id

cl_model_id = config.get("model_id")
    #model = Model(model_id=cl_model_id)
    model = InputModel(model_id=cl_model_id)
    checkpoint = model.get_local_copy()

Hi @<1730033904972206080:profile|FantasticSeaurchin8> , can you attach the log of the task where ClearML SDK fails to resolve the output_uri?
Is the None URL reachable from inside the container running the ClearML task code?

This issue was solved by adding task.output_uri = "fileserver" in the scheduling script, but for some reason this does not work when setting in task.create call in the same script but needs to be set after. it also doesnt work when being set in the training script, so there must have been some unknown overriding

I was away for a week, was anyone able to come up with nay solutions for this?